Radical Numerics says its genome model can continue an aptamer optimization trajectory
The company's self-reported claim that its GLM reproduced held-out high scores from low-scoring RNA sequences only; no independent verification yet.
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Radical Numerics CEO Eric Nguyen said in the show notes of the September 23 Latent Space episode that his genomic language model, in an aptamer experiment, recapitulated some held-out high-scoring samples after seeing only low-scoring RNA sequences.
Per the episode's show notes, the experiment held out the best-performing aptamers, showed the model only lower-scoring sequences in a rising series, and the model continued the trajectory on its own. Nguyen calls this chain-of-thought "thinking in DNA."
This is a first-party claim: the dataset size, scoring method and full results are not published with the notes, and it should not be read as independently verified performance. Watch for a checkable paper or benchmark.